Harris, Paul and Charlton, Martin and Fotheringham, Stewart
(2010)
Moving window kriging with geographically weighted variograms.
Stochastic Environmental Research and Risk Assessment, 24 (8).
pp. 1193-1209.
ISSN 1436-3240
Abstract
This study adds to our ability to predict the
unknown by empirically assessing the performance of a
novel geostatistical-nonparametric hybrid technique to
provide accurate predictions of the value of an attribute
together with locally-relevant measures of prediction con-
fidence, at point locations for a single realisation spatial
process. The nonstationary variogram technique employed
generalises a moving window kriging (MWK) model
where classic variogram (CV) estimators are replaced
with information-rich, geographically weighted variogram
(GWV) estimators. The GWVs are constructed using ker-
nel smoothing. The resultant and novel MWK–GWV
model is compared with a standard MWK model (MWK–
CV), a standard nonlinear model (Box–Cox kriging, BCK)
and a standard linear model (simple kriging, SK), using
four example datasets. Exploratory local analyses suggest
that each dataset may benefit from a MWK application.
This expectation was broadly confirmed once the models
were applied. Model performance results indicate much
promise in the MWK–GWV model. Situations where a
MWK model is preferred to a BCK model and where a
MWK–GWV model is preferred to a MWK–CV model are
discussed with respect to model performance, parameteri-
sation and complexity; and with respect to sample scale,
information and heterogeneity.
Repository Staff Only(login required)
 |
Item control page |
Downloads per month over past year
Origin of downloads
Altmetric
Altmetric